openHistorian VS awesome-TS-anomaly-detection

What are some alternatives?

autotier - A passthrough FUSE filesystem that intelligently moves files between storage tiers based on frequency of use, file age, and tier fullness.

Awesome-Geospatial - Long list of geospatial tools and resources

Kotori - A flexible data historian based on InfluxDB, Grafana, MQTT, and more. Free, open, simple.

awesome-metric-learning - 😎 A curated list of awesome practical Metric Learning and its applications

questdb.io - The official QuestDB website, database documentation and blog.

Netdata - The open-source observability platform everyone needs

lambdo - Feature engineering and machine learning: together at last!

NAB - The Numenta Anomaly Benchmark

Spreads - Series and Panels for Real-time and Exploratory Analysis of Data Streams

A3 - Inspired by recent advances in coverage-guided analysis of neural networks, we propose a novel anomaly detection method. We show that the hidden activation values contain information useful to distinguish between normal and anomalous samples. Our approach combines three neural networks in a purely data-driven end-to-end model. Based on the activation values in the target network, the alarm network decides if the given sample is normal. Thanks to the anomaly network, our method even works in strict semi-supervised settings. Strong anomaly detection results are achieved on common data sets surpassing current baseline methods. Our semi-supervised anomaly detection method allows to inspect large amounts of data for anomalies across various applications.

sktime - A unified framework for machine learning with time series

pyod - A Comprehensive and Scalable Python Library for Outlier Detection (Anomaly Detection)